code: def numba_funcify_MakeVector(op, node, **kwargs):
      dtype = np.dtype(op.dtype)
  
      global_env = {
          "np": np,
          "to_scalar": numba_basic.to_scalar,
          "dtype": dtype,
      }
  
      unique_names = unique_name_generator(
          ["np", "to_scalar"],
          suffix_sep="_",
      )
      input_names = [
          unique_names(v, force_unique=True) for v in node.inputs
      ]
  
      def create_list_string(x):
          args = ", ".join(
              [f"to_scalar({i})" for i in x]
              + ([""] if len(x) == 1 else [])
          )
          return f"[{args}]"
  
      makevector_def_src = f"""
  def makevector({", ".join(input_names)}):
      return np.array({create_list_string(input_names)}, dtype=dtype)
      """
  
      makevector_fn = compile_function_src(
          makevector_def_src, "makevector", {**globals(), **global_env}
      )
  
      return numba_basic.numba_njit(makevector_fn)
  
file_path: pytensor/pytensor/link/numba/dispatch/tensor_basic.py
imports: from textwrap import indent
  import numpy
  from pytensor.link.numba.dispatch import basic
  from pytensor.link.numba.dispatch.basic import create_tuple_string, numba_funcify
  from pytensor.link.utils import compile_function_src, unique_name_generator
  from pytensor.tensor.basic import Alloc, AllocEmpty, ARange, ExtractDiag, Eye, Join, MakeVector, ScalarFromTensor, Split, TensorFromScalar
  from pytensor.tensor.shape import Unbroadcast
label_desc: Use image node_id 8 for calling a global function with example usage: numba_funcify_MakeVector(op, node) and returns: numba_basic, str, args, str